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Modern mass spectrometry (MS)-based protein identification and characterization relies upon accurate mass measurements of the C-13 isotopic distributions of the enzymatically produced peptides. Interestingly, obtaining peptide elemental composition information from its isotopic fine structure mass spectrum to increase the confidence in peptide and protein identification has not yet been developed into a bottom-up proteomics-grade analytical approach. Here, we discuss the possible utility and limitations of the isotopic fine structure MS for peptide and protein identification. First, we in silica identify the peptides from the E. colt tryptic digest and show the increased confidence in peptide identification by consideration of the isotopic fine structures of these peptides as a function of mass and abundance accuracies. In the following, we demonstrate that the state-of-the-art high magnetic field Fourier transform ion cyclotron resonance (FT-ICR) MS allows a routine acquisition of the isotopic fine structure information of a number of isobaric peptide pairs, including a pair of peptides originating from E. coli. Finally, we address the practical limitation of the isotopic fine structure MS implementation in the time constraint experiments by applying an advanced signal processing technique, filter diagonalization method, to the experimental transients to overcome the resolution barrier set by the typically applied Fourier transformation. We thus demonstrate that the isotopic fine structures of peptides may indeed improve the peptide and possibly protein identification, can be produced in a routine experiment by the state-of-the-art high resolution mass spectrometers, and can be potentially obtained on a chromatographic time-scale of a typical bottom-up proteomics experiment. The latter one requires at least an order of magnitude increase in sensitivity of ion detection, which presumably can be realized using high-field Orbitrap FTMS and/or future generation of ultrahigh magnetic field FT-ICR MS equipped with harmonized ICR cells.
Aïcha Hessler-Wyser, Johann Michler, Amit Sharma, Caroline Hain, Daniele Casari, Thomas Nelis
Thomas Rizzo, Ahmed Ben Faleh, Stephan Warnke, Ali H Abikhodr, Vasyl Yatsyna